In time domain HRV analysis, mean standard deviation of normal-to-normal intervals (SDNN) and root mean square of successive RR interval differences (RMSSD) were significantly lower for cases compared with controls . In the post-acute phase of infection, SARS-CoV-2 produces an autonomic imbalance mirrored by a reduction in HRV. These results are consistent with epidemiological data that suggest a higher risk of acute cardiovascular complications in the first 30 days after COVID-19 infection.Healthcare workers (HCWs) represent a population with a significant burden of paucisymptomatic COVID-19, as the general population. We evaluated autonomic nervous system activity by means of heart rate variability (HRV) in HCWs during health surveillance visits. Short-term electrocardiogram (ECG) recordings were obtained 30 days (IQR 5.25–55.75) after a negative naso-pharyngeal swab for SARS-CoV-2 in 44 cases and compared with ECGs of 44 controls with similar age and sex distribution. Time and frequency domain HRV were evaluated. HCWs who used drugs, had comorbidities that affected HRV, or were hospitalized with severe COVID-19 were excluded. Frequency domain HRV analysis showed a significantly higher low/high-frequency power ratio (LF/HF) in the case study compared with controls (t = 2.84,  Coronavirus disease 2019 (COVID-19) is the illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which has caused over 6 million deaths worldwide  and specA total of 96 unselected HCWs working in the University Hospital of Padua were screened with an HRV assessment. This was done during routine health surveillance carried out in compliance with legislative decree 81/08 and European Community Directive 90/679. Subjects included 48 HCWs in the post-acute phase of infection with a confirmed history of paucisymptomatic or mild COVID-19 infection between March 2021 and April 2022 and 48 control HCWs with similar age and sex distribution. From these subjects, eight HCWs were excluded because the collected electrocardiographic trace was not suitable for analysis . The study population included 20 nightshift HCWs with a confirmed history of COVID-19 among the group of cases and 14 among the group of controls. HCWs who regularly work a full night shift at least 5 times a month were included as night workers. The “night period” started from 8 p.m. and ended 6 a.m. Since all HCWs were periodically screened with a SARS-CoV-2 naso-pharyngeal swab (NPS), as required by hospital protocol, we can reasonably exclude reinfections in the case group and asymptomatic COVID-19 infections in the control group. Subjects were excluded if they had an active COVID-19 infection, history of SARS-CoV-2 infection which required hospitalization or home oxygen treatment, or if they had severe respiratory or other major organ involvement. Subjects were also excluded if they were affected by or have a history of diabetes mellitus, cardiovascular diseases , respiratory diseases , severe obesity, renal failure, thyroid disease, chronic liver disease and systemic inflammatory or autoimmune disorders, neurological disorders , or malignancy. Moreover, subjects using drugs that could interfere with the analysis  were excluded. In the case group, 21 HCWs were fully vaccinated (with two or three doses) before the infection. In the control group, 40 HCWs were fully vaccinated (with two or three doses). All vaccinated subjects received the Pfizer-BioNTech mRNA vaccine (BNT162B2).The study was approved by the local Research Ethics Committee (Protocol number = 267n/AO/22) and conducted in accordance with the ethical principles stated in the “Declaration of Helsinki”. All participants gave informed consent.2) [For the case group, short-term electrocardiogram (ECG) and blood pressure were recorded after a negative NPS for SARS-CoV-2 and after symptoms had disappeared (for at least three days). The median elapsed time from the negative NPS for SARS-CoV-2 to the short-term ECG recording was 30 days IQR 5.25–55.75). Disease duration was defined as the period between the positive NPS (or the onset of symptoms if these were prior to the positive NPS) and the negative NPS for SARS-CoV-2. For controls, short-term ECG and blood pressure were recorded during periodical health checks. Subjects in both groups were instructed to avoid smoking, and to stop coffee and alcohol intake for 2 h and 48 h, respectively. They should have had sufficient (at least 8 h) rest and must not have worked the night shift before the test was performed. Blood pressure was measured once with a sphygmomanometer while the patient was lying calmly. HRV was assessed by an ECG performed in a supine position under physiologically stable conditions and using a device connected to the patient via two electrodes. HRV data were acquired by a Bluetooth acquisition system . ECG was recorded for at least 5 min between 9 a.m. and 2 p.m., at rest and under ideal temperature conditions. HRV was analyzed using Kubios HRV software (ver. 3.3) . Normal 2) . Power s2)  as well 2) ,25, whil–55.75. Dn = 39 experimental subjects and 39 controls. The effect size was calculated for all HRV parameters, with the use of G*Power performing a post-hoc analysis after the completion of the study.Sample size estimation for an unpaired t-test was applied to calculate the sample size. The calculation was computed through a STATA command by specifying a mean difference = 18 and standard deviation of differences = 28 for LF/HF as described in a similar study . The grop-values less than 0.05 were considered significant.Statistical analyses were performed with Minitab, LLC, version 18.0. The Kolmogorov–Smirnov test was performed to evaluate whether the variables were normally distributed. Continuous variables were presented as means ± SE or median (IQR 25–75) and categorical variables as frequency. Data with a wide dispersion were expressed in log transformed values. The Chi-square test with Yates correction was used for categorical variables where appropriate. For continuous data, a Student’s t-test was used for normally distributed variables. The Mann–Whitney U test was performed when indicated. Lastly, the influence of independent variables, including infection with SARS-CoV-2, age, sex, elapsed time from COVID-19 to ECG test, vaccination status, night work, body mass index, systolic blood pressure and palpitations on LF/HF, as dependent variable, was appraised by multiple linear regression analysis. Although the time–domain methods, especially the SDNN and RMSSD methods, can be used to investigate recordings of short durations, the frequency methods are usually able to provide more easily interpretable results in terms of physiological regulations. Therefore, we chose LF/HF as dependent variable in the multivariate analysis. All Characteristics of the study subjects are reported in 2  = 17.3, p = 0.00032). In the case group, the median duration of the COVID-19 acute phase was 15.0 days (IQR 10.2–20.0). At their health surveillance visits, all subjects reported mild to moderate symptoms during the acute phase of the disease. The most commonly reported symptoms are listed in No differences were found between the group of paucisymptomatic HCWs recovered from COVID-19 and the control group regarding age, gender, systolic and diastolic blood pressure, body mass index, and night shiftwork. Overall, 21 HCWs received a full vaccination cycle (with two or three doses) before SARS-CoV-2 infection, while 40 HCWs in the control group carried out the complete vaccination cycle (with two or three doses) . Normalized low frequency power (nLF) was higher in the group of recovered COVID-19 HCWs compared with the group of control HCWs . Thus, the LF/HF ratio was higher in the group of recovered COVID-19 HCWs compared with the group of control HCWs  . Mean resting heart rate (HR) was in the range of normal values in both groups, although it was significantly higher in the group of recovered COVID-19 HCWs compared with the group of control HCWs . In our samples the following effect size were found for each HRV parameter, with a 95% confidence interval: 0.45 with a statistical power of 0.55 for nLF and nHF, 0.61 with a statistical power of 0.80 for LF/HF, 0.58 with a statistical power of 0.76 for SDNN, 0.70 with a statistical power of 0.90 for RMSSD, 0.51 with a statistical power of 0.65 for mean HR.Among the spectral components in the frequency domain HRV parameters, normalized high frequency power (nHF) was lower in the group of recovered COVID-19 HCWs compared with the group of control HCWs  . Among t= 0.006) , both thp = 0.2) and a decrease in mean SDNN  and mean RMSSD  when compared to vaccinated recovered COVID-19 HCWs, although these differences were not statistically significant  and disease duration and elapsed time from COVID-19 to the ECG test . Subgrounificant . Multiplnificant .The main findings stemming from this study on paucisymptomatic COVID-19 HCWs evaluated in the post-acute phase of SARS-CoV-2 infection, when compared with control HCWs with similar age, sex, body mass index, blood pressure distribution, and comparable working conditions show: (1) an imbalance of autonomic cardiac regulation, characterized by a persistent increase in sympathetic activity, mirrored by an increase in nLF and LF/HF and decreased vagal activity, as shown by a reduction in nHF; (2) a reduction in HRV time domain parameters measured by mean SDNN and RMSSD. These results suggest that the persistent increase of sympathetic activity is associated with SARS-CoV-2 infection as well as the related inflammation and could be involved in post-COVID cardiovascular events. To our knowledge, this is the first study identifying a persistent increase of sympathetic drive and a reduction in HRV in paucisymptomatic COVID-19 HCWs in the post-acute phase, i.e., about days 30 after SARS-CoV-2 infection. Three previous studies analyzing sympathetic activity ,17,18 gaViral infections are also known to be a trigger of dysautonomia . DysautoOur work presents some limitations. The sample size was rather small, but the two groups of workers were comparable, so a lot of bias was excluded. Secondly, we did not include severe COVID-19 cases, which represented only 20% of the total . We alsoThe most important findings can be summaries as follows.SARS-CoV-2 is associated with an autonomic imbalance in the post-acute phase after recovery of paucysintomatic COVID-19.The persistent increase of sympathetic activity reflected by a reduction in HRV may explain the epidemiological data on a higher risk of acute cardiovascular complications in the 30 days after SARS-CoV-2 infection.Our results in HCWs, with a significant burden of paucisymptomatic COVID-19 as the general population, may have relevant public health consequences.The simple, non-invasive, and inexpensive short-term HRV measurements can be used during health surveillance to help occupational physicians in issuing better judgments of fitness to work.These measurements can also be used in the follow-ups for early identification of people  that may be at risk of clinical worsening, potentially developing into long-COVID syndrome.Future research should certainly further test whether autonomic imbalance and lower HRV persist in the long-term, and whether they have a role in the development of post-COVID syndrome.


During prenatal life, exposure to synthetic glucocorticoids (SGCs) can alter normal foetal development, resulting in disease later in life. Previously, we have shown alterations in the dendritic cytoarchitecture of Purkinje cells in adolescent rat progeny prenatally exposed to glucocorticoids. However, the molecular mechanisms underlying these alterations remain unclear. A possible molecular candidate whose deregulation may underlie these changes is the glucocorticoid receptor (GR) and neurotrophin 3/ tropomyosin receptor kinase C, neurotrophic complex (NT-3/TrkC), which specifically modulates the development of the neuronal connections in the cerebellar vermis. To date, no evidence has shown that the cerebellar expression levels of this neurotrophic complex are affected by exposure to a synthetic glucocorticoid in utero. Therefore, the first objective of this investigation was to evaluate the expression of GR, NT-3 and TrkC in the cerebellar vermis using immunohistochemistry and western blot techniques by evaluating the progeny during the postnatal stage equivalent to adolescence . Additionally, we evaluated anxiety-like behaviours in progeny using the elevated plus maze and the marble burying test. In addition, an environmental enrichment (EE) can increase the expression of some neurotrophins and has anxiolytic power. Therefore, we wanted to assess whether an EE reversed the long-term alterations induced by prenatal betamethasone exposure. The major findings of this study were as follows: i) prenatal betamethasone (BET) administration decreases GR, NT-3 and TrkC expression in the cerebellar vermis ii) prenatal BET administration decreases GR expression in the cerebellar hemispheres and iii) enhances the anxiety-like behaviours in the same progeny, and iv) exposure to an EE reverses the reduced expression of GR, NT-3 and TrkC in the cerebellar vermis and v) decreases anxiety-like behaviours. In conclusion, an enriched environment applied 18 days post-weaning was able to restabilize GR, NT-3 and TrkC expression levels and reverse anxious behaviours observed in adolescent rats prenatally exposed to betamethasone. Eight multiparous female Sprague–Dawley rats from the animal vivarium of the Department of Chemistry and Pharmacy at the University of Chile were used in this study. All animals were housed under controlled environmental conditions with a consistent temperature (20 ± 1 °C), a standard day–night cycle (12:12 hours light: dark), and food and water available ad libitum. Once they had mated , the females were placed in individual cages (45 × 25 × 20 cm), and gestational Day 0 (G0) was determined by the presence of sperm detected in vaginal smears.−1 Cidoten®; Schering-Plough, Inc., Santiago, Chile) in the dorsal neck region, separated by an 8-hour interval, on G20. The CON and CON-EE mothers were administered an equal volume (1 mL) of saline The pregnant rats were randomly classified into four experimental groups: control (CON) animals, control EE-exposed (CON-EE) animals, betamethasone-treated (BET) animals, and betamethasone-treated and EE-exposed (BET-EE) animals. Pregnant rats were housed in groups of three per cage and were separated into individual cages on the day of delivery. Mothers in the BET and BET-EE groups were administered two subcutaneous injections of BET  2.3.On P52, all groups were assessed for anxiety-like behaviours using two different standardized protocols: EPM and MB tests. In the EPM, each animal was gently placed in the central quadrant of the maze for 5 min (under 300 lux of light with white noise). The number of entries into the open arms of the EPM and the amount of time spent in the open arms were recorded, and the data from the rats that fell from the EPM were discarded. The animals with fewer entries and spent less time in the open arms were considered more anxious 2.4.After behavioural evaluations were performed, male animals were initially anaesthetized with a lethal dose of isoflurane. Subsequently, the rats were decapitated, and the brain was quickly removed. Then, the cerebellum was separated from the remaining brain tissue. Twelve cerebellar tissues per group (six for immunohistochemistry and others six for western blot) were randomly selected to perform the immunohistochemical or western blot analyses in the present study. Six cerebellums were used to perform the immunohistochemistry and another six different cerebellums to perform the western blot. Given that the CON-EE group only had 12 males (the lowest number of progenies obtained between the groups), we carried out the maximum number of experiments that was the same for the two indicated procedures and between the study groups. That is, 6N per group for each immunohistochemical and western blot analysis. Regarding the groups whose progenies were greater than 12 males, 6 cerebellar tissues were randomly chosen to be analyzed by immunohistochemistry and another 6 by western blot. The analysis of the immunohistochemical expression of GR was carried out in the cerebellar vermis (cerebellar lobule IX) and in the right and left cerebellar hemispheres. Whereas the analysis of the immunohistochemical expression of NT-3 and TrkC was carried out only in the cerebellar vermis.2.5.2O2  for 30 min at room temperature. After two additional washes with PBS (1×), the slices were blocked with 3% BSA (Sigma) and 0.4% Triton X-100 (Sigma) for one hour. The primary antibodies used were GR (G-5): sc-393232 (Santa Cruz Antibodies) (1:100); NT-3 (N-20): sc-547393232 (Santa Cruz Antibodies) (1:500) and TrkC: TA328907 (1:200) (OriGene Antibodies). The tissues were separately incubated with primary antibodies in blocking solution overnight at room temperature. The tissues were then washed three times with PBS (1×) and incubated with biotin-conjugated anti-rabbit  (1:500)) and biotin anti-mouse  (1:500)) secondary antibodies diluted in 1.5% BSA and 0.2% Triton X-100 for two hours at room temperature without agitation. Then, the tissue was washed again three times with PBS (1×). The avidin–biotin peroxidase complex for antibody detection  was prepared in 1.5% BSA and Triton X-100 for one hour and added to the substrate with diaminobenzidine for 20 min without stirring to visualize the labelled protein . Finally, the sections were washed with distilled water for 10 s. The cerebellar vermis sections were immediately mounted on slides, dried in air, and covered with Entellan (Merck) prior to cover-slipping. Negative controls were obtained by excluding the primary antibody. The cerebellar vermis sections were coded and observed using a Nikon eclipse CI, software NHS elements D 4.60.00 64bits microscope. Immunoreactivity was quantified using ImageJ software  based on greyscale images . The immunostaining for GR (cerebellar vermis and right and left hemispheres), NT-3 and TrkC proteins in the cerebellar vermis (cerebellar lobule IX) was quantified in 10 images per parasagittal section of tissue (40×) per group. Then, using the ImageJ program , images were captured from selected areas of the vermis (cerebellar lobule IX) and of the hemispheres, and the average grey value  was measured, resulting in a representative optical immunohistochemical staining intensity value for each cerebellar vermis and hemispheres sections. Caution was taken to scan all images under equal lighting conditions. Immunohistochemical analyses were performed by the same investigator who was blinded to the experimental groups of the samples.This analysis was performed by immediately freezing of the corresponding cerebellar vermis in liquid nitrogen. Snap-frozen cerebellar vermis and hemispheres tissues were sectioned (20 µm thick) on a cryostat . The Rat Brain in Stereotaxic Coordinates, Sixth Edition of 2.6.Western blot analyses were conducted by homogenizing the corresponding cerebellar vermis (cerebellar lobule IX) and the right and left cerebellar hemispheres in 1× radioimmunoprecipitation assay (RIPA) buffer  containing protease inhibitors  with 0.5 mM phenylmethanesulfonylfluoride (PMSF) immediately after harvest. Tissue homogenates were centrifuged at 13,000× g for 10 min at 4 °C. The concentrations of proteins in the homogenates were determined using Qubit® assays. The tissue extracts (20 µg of protein) were electrophoresed on 8% acrylamide gels and transferred to nitrocellulose membranes at 350 mA for 1 hour. The membranes were incubated with blocking solution (5% nonfat milk in 1× PBS-Tween) for 1 hour at room temperature followed by an incubation with primary antibodies against GRs, NT-3 and TrkC in blocking solution overnight at 4 °C. The primary antibodies used were GR (G-5): sc 393232 (Santa Cruz Antibodies) (1:100); NT-3 (N-20): sc-547393232 (Santa Cruz Antibodies) (1:1000); and TrkC: TA328907 (OriGene Antibodies) (1:1000). The anti-beta tubulin antibody ab6046 (Abcam Antibodies) was used as a loading control. The secondary antibodies employed were peroxidase-labelled goat anti-rabbit IgG (H + L) at 1:7000  and peroxidase-labelled goat anti-mouse IgG (H + L) at 1:7000  in blocking solution for 1 hour at room temperature. Immunolabelling was detected using a chemiluminescence detection system (WESTAR® Supernova). The densitometric quantification of protein bands was performed using the digital image processing program ImageJ.2.7.First, the distribution of the data was determined using the Shapiro-Wilk test (data not shown). If the data exhibited a normal distribution, one-way ANOVA followed by Tukey's multiple comparisons test or two-way ANOVA by Sidak's or Tukey's multiple comparisons test were performed to assess differences in the variables evaluated between the groups. If the data were not normally distributed, the equivalent nonparametric statistical test  was used followed by Dunn's multiple comparisons test. GraphPad Prism 9  software was used to analyse behavioral, immunohistochemical and western blot data. The results are presented as the means ± standard errors of the means (SEMs). Differences were considered significant at p < 0.05 in this study. In addition, the effect size was calculated for the variables studied In summary, the experimental desing is schematized in 3.3.1.The level of anxiety-like behaviour in all groups studied was assessed using two standardized and widely employed behavioural tests: the EPM . The results were analysed using one-way ANOVA, followed by Bonferroni's multiple comparisons test. BET-treated animals buried significantly more marbles than age-matched animals in the CON and BET-EE groups  = 5,9 **p = 0.015; 3.2.The immunodetection of each of the abovementioned markers was analysed in the molecular layer (ML) and granular layer (GL) of cerebellar vermis sections from each group. Notably, GR immunostaining was also measured in the cerebellar hemispheres. Values obtained from each group were compared using one-way ANOVA followed by Tukey's multiple comparisons test.The comparison of the expression of GR in the ML between the groups studied, showed significant differences, the ANOVA value obtained was p = 0.0084; F = 1,241; size effect = 0.521, medium size effect.The multiple interactions obtained by Tukey, indicated that compared with matched CON animals, BET-treated animals exhibited significantly reduced GR immunoreactivity in the cerebellar vermal ML (*p < 0.05). The expression of GR in the ML of the cerebellar vermis was lower in the BET group than in the CON-EE group (*p < 0.05). The multiple interactions evaluated between the other groups were found to be non-significant .The comparison of the GR expression in the GL between the groups studied, showed significant differences, the ANOVA value obtained was p = 0.0019; F = 1,714; size effect = 0.527, medium size effect. The multiple interactions obtained by Tukey, indicated that GR expression was decreased in the GL of the cerebellar vermis in the BET group compared with the CON group (**p < 0.001) and the CON-EE group (**p < 0.001) and the BET-EE group (*p < 0.05). The multiple interactions evaluated between the other groups were found to be non-significant . In particular, GR expression was evaluated in the molecular layer and granular layer of both cerebellar hemispheres . In the Importantly, regarding the neurotrophic complex analysed, the comparison of the NT-3 expression in the ML between the groups studied, showed significant differences, the ANOVA value obtained was p = 0.0037; F = 0,3420; the size effect was 0.587, which is considered a medium effect. In fact, BET-treated animals exhibited significantly reduced NT-3 immunoreactivity in the cerebellar ML compared with that of matched CON animals (**p < 0.01). Furthermore, a significant decrease in NT-3 expression in the cerebellar ML was also observed in the BET group compared to the CON-EE group (*p < 0.05) and the BET-EE group (*p < 0.05). Moreover, a similar change was observed in NT-3 immunoreactivity in the granular layer, the comparison of the expression of NT-3 in the GL between the groups studied, showed significant differences, the ANOVA value obtained was p = 0.0002; F = 0,4175; the size effect was 0.590, which is considered a medium effect. Compared with matched CON animals, BET-treated animals displayed significantly reduced NT-3 immunoreactivity in the cerebellar GL ; and the size effect was 0.503, which is considered a medium effect. In fact, BET-treated animals exhibited significantly reduced TrkC immunoreactivity in the cerebellar ML compared with that in matched CON animals ; and the size effect was 0.486, which is considered a medium effect. For example, anti-TrkC labelling in the BET group was sparser than that in the control group (*p < 0.05) or the BET-EE group  were analyzed using western blotting to determine GR, NT-3, and TrkC expression levels in the cerebellar vermis and GR expression in the left and right cerebellar hemispheres. Bands for the GR, NT-3, and TrkC proteins  were observed and quantified using densitometry. The levels of each of the proteins described above were normalized to levels of the total protein loading control β-tubulin. The levels of each of the molecular markers described above in the control group were set to 100%, and the levels in all the other groups were calculated as a percentage of this expression level.As in the previous analyses, the data obtained were subjected to the Shapiro-Wilk normality test. The distributions of the data obtained from western blotting for all the analyzed markers were not normally distributed (data not shown); thus, the subsequent statistical analyses were performed with the Kruskal-Wallis test, followed by comparisons between the analyzed groups using Dunn's test.In this context, BET-treated animals displayed significantly lower GR expression than animals in the CON (**p < 0.001), CON-EE (*p < 0.05), and BET-EE (*p < 0.05) groups  and CON-EE (*p = 0.0290) groups. The graph showing the data is accompanied by representative images .Regarding the NT-3/TrkC complex, NT-3 was expressed at significantly lower levels in BET-treated animals than in matched animals from the CON (*p = 0.0123), CON-EE (*p = 0.0329) and BET-EE (*p = 0.0309) group. Finally, in the BET group, TrkC was expressed at a lower level than that in the CON group (**p = 0.0061) and the BET-EE group (*p = 0.0131). The graphs showing the data are accompanied by the corresponding representative images in 4.We showed that progeny exposed to BET prenatally showed an increase in anxiety-like behaviors, as evaluated using the EPM and the MB test. Rats in the BET group stayed in the enclosed arms of the EPM longer (compared to the CON EE group) and hid more marbles in the MB test than the control group. These results are consistent with previously reported data The changes in GR expression level observed in the BET group are consistent with previous studies showing a decrease in the level of GR expression in the limbic structures of offspring subjected to antenatal SGCs exposure Considering the determinant regulatory role of NT-3 in cerebellar tissue, we suggest that a decrease in NT-3 expression might be involved in alterations in the differentiation and elaboration of the dendritic trees of PCs described in our previous studies On the other hand, we wanted to evaluate whether an EE was able to reverse the alterations described above. Regarding the effect of EE on anxiety-like behaviours observed in the group exposed to prenatal stress, the BET-EE group hid fewer marbles than the BET group in the MB test. These results are consistent with several studies describing the anxiolytic effect of an EE. For example, EE exposure reduces anxiety-like behaviours and prevents cognitive impairment elicited by acute and chronic stressors Regarding the restorative effect of an EE on GR expression observed in the present study, Ashokan et al. (2018) In addition, EE exposure has been suggested to be a key factor promoting NT-3 expression Finally, we have shown that short-term exposure (18 days) to an EE restores all the alterations associated with BET exposure described in this study, consistent with our most recent study showing that the same EE restored the expression of various synaptic cerebellar proteins and reversed locomotor problems caused by prenatal exposure to BET 5.In this study we observed that the exposure to prenatal BET (i) increased the anxiety-like behaviors (ii) decreased the expression of GR, NT-3 and TrkC, in the cerebellar vermis; (iii) decreased the expression of GR in both cerebellar hemispheres, and that the EE provided in the present study minimized all the previously mentioned alterations. Finally, the effect of prenatal stress on the cerebellum, and the effect of EE to reduce these alterations should continue to be studied.


Whether long-term symptom improvement is maintained after treatment in services such as the Norwegian Prompt Mental Health Care (PMHC) and the English Improving Access to Psychological Therapies is not yet known. In this prospective study, we investigate whether improvements observed at 6-month follow-up are maintained at 24- and 36-month follow-up among clients who received PMHC.n = 459). The main outcomes were (reliable) recovery rate and symptoms of depression (PHQ-9) and anxiety (GAD-7). Primary outcome data at 24- and 36-months follow-up were available for 47% and 39% of participants, respectively. Secondary outcomes were work participation, functional status, health-related quality of life, and positive mental well-being. Sensitivity analyses with regard to missing data assumptions were conducted for the primary continuous outcomes.Data from the treatment arm of the randomized controlled trial of PMHC were used  recovery rate, and health-related quality of life. Small linear improvements since 6-month follow-up were observed for work participation, functional status, and positive mental well-being. Sensitivity analyses did not substantially alter the findings for symptoms of depression and anxiety mentioned above.Our findings support the long-term effectiveness of PMHC, but results should be interpreted with caution due to lacking follow-up data at 24- and 36-month in the control group, and substantial attrition. Cognitive behavioural therapy (CBT) is effective in treating depression and anxiety –8. HowevThe service Prompt Mental Health Care (PMHC) was initiated in order to improve access to evidence-based primary care treatment for individuals with symptoms of mild to moderate depression and anxiety disorders in Norway , 18. PMHResearch has shown that IAPT treatment is associated with substantial symptom reduction , 24. HowThe aim of the present study was therefore to determine whether the observed improvement at 6-month follow-up was maintained at 24- and 36-month follow-up with regard to primary and secondary outcomes for clients who were assigned to the PMHC treatment condition. Sensitivity analyses were conducted in order to explore the impact of missing data assumptions on results for continuous primary outcomes in the presence of attrition.The current study uses primarily data from the treatment arm of an RCT  comparing PMHC to TAU . The RCTEligibility for the PMHC service is based on a defined set of inclusion and exclusion criteria, which were also applied in the trial . The maiAll clients contacting PMHC in Sandnes and Kristiansand got an appointment for individual assessment at the PMHC clinic. As described in our earlier work , the then = 459), while descriptive statistics from the control arm were only used for illustrative purposes (n = 215).Clients who agreed to participate were asked to register to a secure online data portal specifically developed for the evaluation of PMHC by the Norwegian Centre for Research Data (NSD). When registered, the participants filled in the baseline questionnaire. Following completion, the participants were randomized to intervention and control based on a 70:30 ratio. In total, 774 clients participated in the trial and similar to previous studies only clients scoring above cut-off on symptoms of depression and/or anxiety were included [Details on the interventions are described previously , 30, 31,Care in PMHC is based on cognitive behavioral therapy (CBT) , 22. AllIn the PMHC group, clients took part in a median of 5 (IQR = 4–9) treatment sessions . Less thCare in the TAU potentially included all ordinary services available to the target population. Examples are follow-up by the GP, private psychologists and occupational health services. In the letter where TAU clients were informed about their allocation, they were also encouraged to contact their GP for further follow-up. References to publicly available self-help resources  were also included.A year after PMHC, about 1 in 4 of respondents in the PMHC group reported to have (since baseline) received additional care for their mental health problem from outside of PMHC . For 12%n = 381) and 3-month follow-up (n = 223) were constructed for the PMHC group based on the questionnaires that were completed prior to each session. For the 1.5-month follow-up, the last observed measurement prior to 10 weeks after baseline was used and resulted in a variable with a median time since baseline value of 6.3 weeks. For the 3-month follow-up, the last observed measurement between 10 and 14 weeks after baseline was used. For clients who terminated treatment prior to 10 weeks, the posttreatment score was carried forward to 3-month follow-up under the assumption of short-term stability, similar to the procedure that was used in earlier work [n = 416) implying that there were just 43 participants with baseline data only. Clients assigned to the TAU group were asked to complete questionnaires at 3-, 6- and 12-month follow-up only [Clients assigned to the PMHC group were asked to complete questionnaires before each session during the treatment, after treatment, and at 6-, 12-, 24- and 36-month follow-up. To maximize use of available data, proxies for 1.5-month follow-up  , 35. ThiSymptoms of anxiety were measured using the Generalized Anxiety Disorder Assessment (GAD-7) , 36. GADRecovery was defined as scoring above the caseness threshold on the PHQ-9 (≥ 10) and/or GAD-7 (≥ 8) measures at the start of treatment and below the caseness threshold on both these measures at follow-up. The reliable recovery rate was calculated to account for measurement error, aligning with the procedures employed for the IAPT evaluations . Using tFunctional status was measured using the Work and Social Adjustment Scale (WSAS) . WSAS coHealth-related quality of life (HRQL) was measured using the Norwegian version of the EQ-5D-5L , 41. ThePositive mental wellbeing was measured using the Short Warwick Edinburgh Mental Well-Being Scale, sWEMWBS . The sWEWork participation was assessed by means of two questions, one multiresponse item about current work status and one multiresponse item about sources of income. Based on these two questions, it was determined whether participants were in full- or part-time regular work without receiving benefits or not (coded as a binary variable).n = 116/106), that is 6-month to 24-month, and 6-month to 36-month relapse rates. To be counted as a relapse event, symptom scores at 24- and 36-month follow-up for at least one of the outcome measures were (1) above level for caseness and were (2) ≥ 6 (PHQ-9) or ≥ 5 (GAD-7) points greater than the symptom scores at 6-months follow-up. A similar definition was used in previous study examining relapse rates in IAPT [To examine relapse rates at 24- and 36-months follow-up, we included clients that started treatment with case-level depression and/or anxiety symptoms who were reliably recovered at 6-month follow-up and completed PHQ-9 and GAD-7 at 24- and 36-month follow-up  was included as a fixed effect. Multiple imputation was used to estimate (reliable) recovery rates at follow-up. In the first step, 200 datasets containing fourteen variables  were generated using Bayesian analysis . In the second step, (reliable) recovery was conditioned on site using robust maximum likelihood, and model estimates were used to derive (reliable) recovery rates in the PMHC group at follow-up. Model constraints were used to determine whether (reliable) recovery rates were significantly different at 24- and 36-month follow-up compared to 6-month follow-up.Latent growth models were used to model the course of outcome measures over time. Non-linearity was initially modelled by means of quadratic and cubic slopes. Piecewise models and other time transformations were also considered  when model fit was poor. Model fit was assessed by using the Comparative Fit Index (CFI) and the Root Mean Square Error of Approximation (RMSEA). CFI ≥ 0.95, and RMSEA ≤ 0.06 were considered indicative of good model fit . For conSensitivity analyses were performed for the continuous outcome measures of depression and anxiety to examine the impact of missing data at follow-up under various missing not at random (MNAR) conditions, employing both pattern mixture and selection models . These mThe demographic characteristics of individuals taking part in this RCT have already been published , 31. In The percentage of clients in the PMHC group that reported to have received help for their mental health problems from other services since inclusion in PMHC was 24.9%, 33.0%, and 32.3% at respectively 12-, 24- and 36-month follow-up. Additional specialist care by a psychologist/psychiatrist was received by 12.2%, 21.0%, and 20.0% of the clients at respectively 12-, 24- and 36-month follow-up. The remaining clients received help from their GP or from other services at the municipality level.24m = 63.4%, 95%CI 57.9–69.1; RR36m = 66.2%, 95%CI 60.1–72.2) remained unchanged as compared to the estimated rate at 6-month follow-up ; all p > 0.05). A similar pattern was found for the estimated reliable recovery rates . Due to the high degree of missingness at 24- and 36-month follow-up, the reported estimates based on imputed data should be interpreted with some caution. For comparison’s sake, we also provide the observed recovery rates: recovery rate at 24-months = 69.1%, reliable recovery rate at 24-months = 63.4%, recovery rate at 36-months = 69.1%, reliable recovery rate at 36-months = 63.4%.Previous work showed that the recovery rates at 6- and 12-month follow-up were significantly higher in the PMHC group compared to the TAU group . Analysp > 0.05). The estimated linear slopes between 6-month and 36-month follow-up were -0.01  for PHQ and -0.02  for GAD. Point estimates of the within-group effect sizes from 6-month follow-up and beyond with reference to baseline varied between -1.64 and -1.70 for PHQ, and between -1.50 and -1.61 for GAD . Fit statistics for the model with symptoms of depression as outcome were RMSEA = 0.051 and CFI = 0.948, and for the model with symptoms of anxiety as outcome RMSEA = 0.065 and CFI = 0.909. As visualized in Fig. The courses of health-related quality of life and positive mental well-being were modelled in the same way as symptoms of anxiety and depression and gave fit statistics of RMSEA = 0.058/ CFI = 0.942 and RMSEA = 0.053/ CFI = 0.952, respectively. The course of functional status was modelled by means of a piecewise growth curve model as well, but without a time transformation and both parts (prior and after six months) were modelled by two sets of linear and quadratic slopes. As both parts were only based on three timepoints each, the quadratic slope variances were restricted to zero for identification purposes (RMSEA = 0.028/ CFI = 0.994). Due to issues with multicollinearity, measurements at 1.5- and 3-month follow-up were not included to determine the course of work participation. The latter outcome was subsequently modelled by a simple linear growth model  with a log-transformed time scale (RMSEA = 0.026/ CFI = 0.997).p = 0.09), and a mean change score of 2.50 at 36-month follow-up (p < 0.001), all compared to 6-month follow-up. Positive mental well-being also continued to improve at 24-month follow-up with a mean change score of 0.77 (p < 0.001), and at 36-month follow-up with a mean change score of 1.29 (p < 0.001). For health-related quality of life, the estimated change at 24- and 36-month follow-up as compared to 6-month follow-up was not statistically significant (both p > 0.05). This was also the case when examining item 5 of the EQ-5D-5L separately. See Table For functional status, the WSAS score continued to improve with a mean change score of 1.14 at 24-month follow-up (p < 0.05). As the latter two measurement points were purely observational, the increases cannot simply be attributed to the effect of the intervention, as is true for all outcomes in this study.In previous work, we did not find evidence for an effect of PMHC on work participation up to 12-month follow-up because a similar increase in work participation was observed in both the intervention and control group . The datThe relapse rate in the PMHC group was 9.5%  from 6-month to 24-month follow-up and 14.2%  from 6-month to 36-month follow-up.The results of the sensitivity analyses are shown in Fig. Previous research has suggested that PMHC is an effective treatment for people suffering from anxiety and mild-to-moderate depression, and that this effect lasts up to at least 12-month follow-up. This current study adds to this by demonstrating that the observed symptom improvements are maintained or further improve at 24- and 36-month follow-up for those assigned to the PMHC treatment condition. Compared to the change observed between baseline and 6-month follow-up, there was no significant additional change at 24- and 36-month follow-up for symptoms of anxiety and depression, (reliable) recovery rate, and health-related quality of life. For functional status, positive mental well-being, and work participation, further improvements were noted at long-term follow-up. The relapse rate at 24-month follow-up was similar to the rate at 12-month follow-up, while there was a small increase at 36-month follow-up. Results were similar also after considering the possibility of data missing not at random, using pattern mixture and selection models.Very few studies have so far examined the long-term effectiveness of CBT in comparable settings like IAPT and PMHC. Von Brachel et al.  conducteFor three outcomes, a further improvement since 6-month follow-up was observed, most notably for functional status and work participation. Although improvements beyond treatment termination is not uncommon , it’s noThe main strengths of this study are the long follow-up time, the relatively large sample size, the use of validated and reliable questionnaires, the use of modern statistical techniques that ensure that all study participants are included in the analyses despite non-response at follow-up, and the consideration of models under MNAR conditions.The most important sources of potential bias were lack of long-term follow-up data in the control group and substantial attrition. With regard to the latter, all sensitivity analyses pointed in the same direction and had effect sizes of similar magnitude suggesting that the impact of attrition may not be that large. On the other hand, we only test some of all potential MNAR scenarios, and it’s important to note that all these scenarios are based on untestable assumptions. Given the level of attrition in the present study, our findings should therefore nevertheless be interpreted with caution.The current study suggests that PMHC can produce long-lasting improvements in symptoms, function, mental well-being and health-related quality of life. These are all aspects central to the individual and to the economic evaluation of care. As such, the study adds further evidence that this version of IAPT can be considered a viable supplement to existing health services.


Theileria parva is the causative agent of East Coast fever, usually a fatal disease for cattle, which is prevalent in large areas of eastern, central, and southern Africa. Protective immunity against T. parva is mediated by CD8+ T cells, with CD4+ T-cells thought to be important in facilitating the full maturation and development of the CD8+ T-cell response. T. parva has a large proteome, with >4000 protein-coding genes, making T-cell antigen identification using conventional screening approaches laborious and expensive. To date, only a limited number of T-cell antigens have been described. Novel approaches for identifying candidate antigens for T. parva are required to replace and/or complement those currently employed. In this study, we report on the use of immunopeptidomics to study the repertoire of T. parva peptides presented by both BoLA-I and BoLA-DR molecules on infected cells. The study reports on peptides identified from the analysis of 13 BoLA-I and 6 BoLA-DR datasets covering a range of different BoLA genotypes. This represents the most comprehensive immunopeptidomic dataset available for any eukaryotic pathogen to date. Examination of the immunopeptidome data suggested the presence of a large number of coprecipitated and non-MHC-binding peptides. As part of the work, a pipeline to curate the datasets to remove these peptides was developed and used to generate a final list of 74 BoLA-I and 15 BoLA-DR-presented peptides. Together, the data demonstrated the utility of immunopeptidomics as a method to identify novel T-cell antigens for T. parva and the importance of careful curation and the application of high-quality immunoinformatics to parse the data generated.The apicomplexan parasite  Theileria parva, the causative agent of East Coast fever (ECF), a highly pathogenic disease for cattle that is prevalent in large areas of eastern, central, and southern Africa. ECF is estimated to kill ~1 million cattle a year and inflict an annual economic cost of up to 600 million USD [T. parva is transmitted by the brown-eared tick , which deposits sporozoites into the skin of the cattle host while taking a blood meal. The sporozoites rapidly invade host lymphocytes and, once within cells, transition to a schizont form. Schizont-infected cells undergo transformation and are induced into a phase of sustained proliferation, during which infection with the parasite is maintained in the ‘daughter’ cells; dissemination of these cells and the host’s inflammatory response ultimately lead to the clinical symptoms of the disease, and frequently the death of the infected animal. A major challenge to the development of novel vaccines for complex intracellular pathogens is the identification of relevant T-cell antigens. One example of this is + T-cell responses [+ T-cell antigens are a major target for the development of efficacious subunit vaccines. The role of CD4+ T-cells in promoting the induction and maintenance of functionally mature CD8+ T-cells suggests that antigens presented by BoLA class II molecules (BoLA-II) will also be required for an effective CD8+ T-cell-targeting vaccine [+/CD4+ T-cell antigens for inclusion in a vaccine will require consideration of both the diversity of the MHCI and the MHCII genotypes in the target cattle populations and the antigenic variability between different T. parva strains [Currently, ECF control options are limited to the intensive use of acaricides, a single theilericidal chemotherapeutic agent, and an ‘infection-and-treatment’ (ITM) form of immunisation, which are used either alone or in combination. However, none of these options is optimal or sustainable in the longer term due to, among other reasons, increasing acaricide resistance, the expense and poor efficacy of the theilericidal agent, and the logistical issues of deploying a live vaccine, which requires an extensive liquid nitrogen cold chain, in rural Africa. Thus, alternative forms of control are actively being sought. Predominant among these is the development of novel subunit vaccines. Studies on the immune responses generated by ITM immunisation have demonstrated that protection is associated with major histocompatibility complex class I (MHCI)-restricted CD8esponses ,3,4,5. T vaccine ,7. Selec strains ,10,11. CT. parva cDNA and/or peptide libraries) to identify candidate antigens recognised by T. parva-specific T-cells from immune animals have resulted in the identification of a limited number of CD8+ T-cell antigens (Tp1-11 and Tp32-35) from the Muguga genome-reference strain of T. parva [T. parva Muguga-specific CD4+ T-cells [T. parva antigens, and due to the diversity of both the bovine MHC and the parasite variants present ‘in the field’; these epitopes would be anticipated to only provide protection to a proportion of the cattle populations and only against a subset of T. parva strains. The expression of >4000 proteins by T. parva schizonts [T. parva proteome across multiple strains and MHC haplotypes that would be required to generate a comprehensive list of antigens. Such efforts are further impeded by the marked immunodominance that is characteristic of the CD8+ T-cell responses against T. parva [Intensive efforts applying conventional antigen-screening approaches  used in peptidomics have significantly increased the sensitivity, dynamic range, and mass accuracy of the technique, such that the analysis of peptides is orders of magnitude superior to that possible a decade ago. Application of this technique in peptide-MHC (pMHC) elution studies has greatly enhanced our understanding of the repertoire of MHC-bound peptides (‘immunopeptidome’) under steady-state conditions and following neoplastic transformation . ishmania ,23,24,25T. parva-derived peptides presented by infected cells and demonstrate how these mass-spectrometry data, combined with immuno-informatics, can be used as an alternative approach to formulate a list of new T. parva candidate antigens. To the authors’ knowledge, this is the first time immunopeptidomics has been used to study a bovine pathogen, and the first time the immunopeptidome of a protozoan pathogen has been studied in the context of multiple MHCI and MHCII haplotypes. We identified a total of 74 and 15 unique BoLA-I and BoLA-DR-restricted peptides—demonstrating the capacity to use this approach to rapidly characterise the immunopeptidome of T. parva-infected cells and so identify novel candidate CD8+ and CD4+ T-cell antigens.Herein, we report on an immunopeptidomic analysis of Theileria parva-infected B- and T-cell lines were generated and characterised as part of previous studies and maintained using well-established protocols [Cell lines were established from animals for which BoLA-I and BoLA-DR genotypes had been determined by a combination of conventional Sanger-based sequencing of amplicons generated using MHCI-allele-specific PCR , Sanger-rotocols . The cel8 cells/mL for 1 min, diluted with PBS 1:1 and solubilized for 45 min at 4 °C. Lysates were cleared with two-step centrifugation at 500× g for 15 min, followed by 15,000× g for 45 min at 4 °C. pBoLA-I complexes were captured directly from the lysate using a pan-specific anti-BoLA-I antibody (ILA-88) covalently conjugated to protein A sepharose immunoresin  at a concentration of 5 mg/mL. BoLA-DR complexes were captured from the lysate, following a preliminary removal of pBoLA-I complexes (using BoLA-I capture as described above), using a pan-specific anti-BoLA-DR antibody (ILA-21) conjugated to protein A sepharose immunoresin at a concentration of 5 mg/mL. Captured pBoLA-I and pBoLA-DR complexes were washed sequentially using buffers of 50 mM Tris buffer, pH 8.0 containing 150 mM NaCl, then 400 mM NaCl, and finally 0 mM NaCl, prior to elution of the BoLA-bound peptides, BoLA protein chains, and β2M in 10% acetic acid and stored as described previously [Cells were harvested whilst in a log growth phase, and trypan blue staining was used to verify that >95% of the cells were viable at the point of harvest. Cells were washed twice with ice-cold PBS and then lysed in buffer ) at a density of 2 × 10Affinity column-eluted material was resuspended in 120 μL loading buffer  and loaded onto a 4.6 × 50 mm ProSwiftTM RP-1S column  for reverse-phase chromatography on an Ultimate 3000 HPLC system (Thermo Scientific). Elution was performed using a 0.5 mL/min flow rate for over 5 min on a gradient of 2–35% buffer B (0.1% formic acid in acetonitrile) in buffer A (0.1% formic acid). Eluted fractions were collected from 1 to 8.5 min, for 30 s each. Protein detection was performed at 280 nm. Even and odd eluted fractions were pooled together, vacuum, dried, and stored at −80 °C until use.Tm TribridTm Mass Spectrometer or a Q Exactive™ HF-X Hybrid Quadrupole-Orbitrap™ Mass Spectrometer (Thermo Scientific). Peptides were separated on a 75 μm × 50 cm PepMap C18 column using a 1 or 2 h linear gradient from 2–5% buffer A to 35% buffer B at a flow rate of 250 nL/min (approx. 600 bar at 40 °C). Peptides were introduced into the mass spectrometer using a nano Easy Spray source (Thermo Scientific) at 2000 V. The ion transfer tube temperature was set to either 305 °C (Fusion Lumos), or 250 °C (HF-X). Subsequent isolation and higher-energy C-trap dissociation (HCD) were induced on the 20 most abundant ions per full MS scan with an accumulation time of 128 ms and an isolation width of 1.2 Da (Fusion Lumos) or 1.6 Da (HF-X). All fragmented precursor ions were actively excluded from repeated selection for 8 s (Fusion) or 15 s (HF-X). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository [Samples were suspended in a 20 μL loading buffer and analysed on an Ultimate 3000 nano UPLC system online coupled to either an Orbitrap Fusionpository  with theT. parva Muguga proteome [T. parva peptides had to meet the following criteria: (1) a peptide-spectrum matching score (−10 lgP) of >20, (2) no predicted post-translational modifications, (3) a minimum of 2 amino acids difference from any bovine peptide sequence, and (4) for BoLA-DR peptides, predicted to not be a BoLA-I-binding peptide  and 4084 entries for the proteome . The spedatasets ). PredicCpan-4.1  for BoLACpan-4.1  for BoLAT. annulata cells (used as antigen-presented cells −2 × 104 cells per well) were loaded with peptides at a concentration of 5 μg/mL for 2 h. At 37 °C before CD8+ T-cells were added at a density of 1 × 104 cells/well. Plates were incubated at 37 °C for 20 h before washing and identification of IFNG-producing cells by the addition of a biotinylated detection anti-IFNG antibody (CC302b—produced in-house) and development by sequential use of Vectastain  and AEC substrate  solutions. Analysis of spot-forming units (SFU) was completed using an AID automated ELISpot reader .IFNG ELISPOT was conducted using a standard format. In brief, a capture anti-IFNG monoclonal antibody  was bound to a pre-wetted PVF membrane multiscreen plate . Autologous 125I-radiolabeled beta-2-microglubulin was monitored in real-time by a high-throughput scintillation proximity assay, and the half-life of the dissociation was determined. Peptides that failed to bind to BoLA-I molecules did not register a half-life period; the length of the half-life was used to infer the stability and binding strength of individual peptide-BoLA-I complexes.The extracellular domains  of BoLA-I heavy chain molecules had previously been produced as recombinant proteins and used to measure the affinity of peptide-BoLA-I interactions using human beta-2-microglubulin as the light chain component ,35. A prT. parva-derived BoLA-I-associated peptides using an immunopeptidomics approach, BoLA-I-associated peptides were purified with three T. parva-infected cell lines (TP), and the peptide fractions were analysed by LC-MS. Each TP was homozygous for a different MHC haplotype, which expressed 1, 2, or 4 BoLA-I genes  different from a matching sequence in the bovine proteome were excluded. After filtering, 25 (0.32%), 18 (0.25%), and 25 (0.36%) peptide sequences were identified as being unambiguously derived from T. parva proteins in the 1011TP, 2229TP, and 641TP cell lines, respectively . The spectra obtained from the synthetic peptides matched those measured for the majority of the peptides analysed , confirming their correct identification  length distribution, and the low percentage of T. parva peptides predicted to be BoLA-I binders appeared to be due, in part, to the presence of a substantial fraction of peptides  that were longer than the canonical 8–12 a.a. length of MHCI-binding peptides (n = 3/32) were predicted to be BoLA-I binders, whereas 55.6% (n = 20/36) of the 8–12 mer peptides had a predicted rank binding score of <2% , some of which were present in multiple samples despite the disparity in the BoLA-I molecules expressed by the three cell lines, was suggestive that a substantial proportion of the longer peptides were coprecipitants rather than peptides eluted from the peptide-binding groove of the purified BoLA-I molecules. Notably, unlike the bovine-derived peptides, the peptides D. Only 9e of <2% . This ov5.6% n = /36 of thT. parva peptides to bind to BoLA-I molecules in vitro, binding assays were performed on a subset (n = 19) of peptides . Responses were also detected against one of the peptides identified from the pMHCI-elution dataset, TpMuguga_02g0089567–81, which contained the previously identified Tp9 epitope recognised by BoLA-A14+ animals (data not shown), however, no responses were elicited by any of the other peptides tested.ELISPOT assays using established in vitro autologous T. parva peptides were identified, of which 23 were predicted to be BoLA-I binders. Data from spectral matching, BoLA-I binding prediction, and in vitro binding assays confirmed the identity of the pMHCI-eluted peptides and their capacity to bind to the relevant BoLA-I molecules; however, ELISPOT data indicated that only 1/33 of the assayed peptides were recognised by T. parva-specific CD8+ T-cells derived from an ITM-immunised donor.Thus, from this primary set of samples, a total of 68 T. parva-derived peptides, a second sample set comprising 10 TP cell lines was studied. This included four cell lines, which between them expressed four additional BoLA-I haplotypes: 2824TP , 5350TP , 2408TP , and 2123TP ; independent replicate samples derived from cell lines 641TP, 2824TP, 2123TP, 5350TP (denoted by the suffix ‘_rpt’), and duplicate samples from an additional BoLA-A10 cell line (5072TP).Having verified the capacity of LC-MS analysis of BoLA-I-eluted peptides to identify BoLA-I-presented T. parva peptides (average = 46) were identified in each sample , whilst >90% of the bovine peptides were of the canonical 8–12 a.a. length, suggesting the anomalous profile was parasite-specific  and 6–107 h sample A; thus, specific B. Simila binders C.Theileria parva-infected cell lines generated a consistent data profile that comprised subsets of peptides of canonical length, of which ~50% were predicted to be MHCI-binders, and peptides of anomalous lengths that contained few MHCI-binders.Together, the data indicate that BoLA-I immunopeptidomic analysis of T. parva-peptides identified, accounting for 27.1%, 19.3%, and 16.2% of the peptide repertoire, respectively. The majority of peptides derived from TpM_02g00758 were of an anomalous length , were predominantly overlapping peptides originating from a small 30 a.a. region (79/85 peptides (92.9%) derived from TpM_02g00758547–577), and had a poor percent rank binding prediction score . Examination of the collated data derived from the 13 samples demonstrated that ribosomal proteins, histones, and TpM_02g00758 were dominant sources for the 524 1–14) and histone H2A variant 1 (TpMuguga_02g00611), as shown in Peptides from other proteins exhibited similar, but less pronounced, characteristics—for example, ribosomal protein S28-B 40s  where one or more of the peptides was not predicted to be a BoLA-I binder (defined as a rank-predicted binding score >20%). When applied to the combined dataset, this refining process removed 55.2% of the n = 257/289). Based on the high representation of these proteins in the coprecipitation pool, it was decided to remove all peptides derived from these classes of proteins. This resulted in the removal of an additional 75 peptides, so that a total of 364 peptides were excluded; this peptide set had an average length of 14 a.a., a median predicted-rank score of 22.8%, and 68 peptides (18.7%) of the peptides had a rank prediction score of <2%. The retained peptide dataset consisted of 160 peptides, with an average peptide length of 11.6 a.a., a median rank predicted binding score of 2.03%, and 80 peptides (50%) that were predicted to be binders. Thus, the removal of all peptides derived from ribosomes, histones, and TpM_02g00758 caused a slight deterioration in the statistics of the retained peptide set but was considered a good compromise to decrease the retention of possible coprecipitant artefacts. As a final step to refine the peptide dataset, an immunoinformatics filter was used, and all remaining peptides that had a predicted percent rank binding score of >2%  were removed. This left a final dataset of 80 peptides, which, after consolidation of overlapping, nesting, and duplicate identifications, resulted in 74 unique peptides from 68 proteins  and in the triplicate datasets from TP cell lines expressing BoLA-A10 and BoLA-A15 haplotypes .In the final dataset, the average number of T. parva peptides identified in replicate samples was 7.3%; in comparison, the overlap between the bovine peptidomes from the same samples was greater, with 47.1% of bovine peptides identified in replicate samples. The low level of overlap observed between replicate T. parva immunopeptidomes is most likely a consequence of the low number of T. parva peptides identified ; however, the identification of a subset of T. parva peptides in replicate samples indicates that the immunopeptidomes described in this study are at least partially reproducible, and higher resolution studies, yielding greater depth of peptide repertoire coverage, would likely produce datasets exhibiting greater reproducibility.For the BoLA-A18, A10, and A15 groups, there was partial, but limited, overlap between replicate samples; in contrast, for the BoLA-A19 and BoLA-A20 groups, there was no overlap between the samples . As a suT. parva-infected cell lines: 2123TP (BoLA-DR 15:01/11:01), 2824TP (BoLA-DR 16:01), 5072TP (BoLA-DR 10:01), 641TP (BoLA-DR 20:02), 495TP (BoLA-DR 10:01/11:01), and 5350TP (BoLA-DR 12:01), which between them expressed six different BoLA-DR molecules  than the bovine peptides (15.7 a.a)  B and adhT. parva peptides in the BoLA-DR dataset was the dominant representation of peptides from a small subset of proteins. This included TpMuguga_02g00758, from which peptides were identified in all six samples and which accounted for a total of 118 peptides . As with the BoLA-I data, the peptides from these proteins identified in different samples were often clustered in specific regions, were overlapping, and predominantly had poor predicted percent rank binding scores, indicative of coprecipitating peptides. Application of the same process as described for the BoLA-I data to identify putative coprecipitants suggested that a substantial majority of the T. parva peptides , however, the distribution of the retained peptides showed a clear bimodal pattern with peaks of peptides with a rank percent prediction score of <5% and >95%; in contrast, the profile of the coprecipitated  protein .+ and CD8+ T-cell epitopes with a peptide library covering 502 T. parva proteins from the reference Muguga strain [+ T-cell antigens, 10 CD8+ T-cell antigens, and six antigens containing epitopes for both CD4+ and CD8+ T-cells [+ or CD4+ T-cell epitopes, five of the proteins have been identified as T-cell antigens , with an infection rate of ~100%, from the target host species. In malaria, the application of immunopeptidomics has been hindered by the low level of hepatocyte infection (<10%) that can be achieved [T. parva is that the in vitro infected cells express high levels of both BoLA-I and BoLA-II and are of the same phenotype as the cells infected in vivo . The capacity of these T. parva-infected cells to recall antigen-specific T-cells from recovered animals without the need for supplementary APCs confirms that the peptides presented in the context of BoLA-I/BoLA-II by T. parva-infected cells are those that stimulate T-cell responses [T. parva are highly appropriate for studies to apply immunopeptidomics and avoid the potential complication of cell types selected for analysis that was observed in recent Chlamydia studies, where dendritic and epithelial cells were found to have discordant and nonoverlapping immunopeptidomes [Although the large proteome and high parasite diversity are disadvantages in conventional antigen-screening approaches, the biology of achieved . The secesponses ,29. As sptidomes . T. parva to analyse the immunopeptidomes from a total of nine different cell lines representing a range of BoLA-I haplotypes and BoLA-DR molecules. This gave our study a unique structure compared to immunopeptidomic studies that have been reported for other eukaryotic parasites , where data have been generated for either MHCI or MHCII and from only single samples [+/CD8+ T-cell candidate antigen identification for a T. parva vaccine that will be used in outbred cattle populations. As such, there was an interest in generating data for both BoLA-I and BoLA-II molecules for a range of different genotypes. In this study, we purposely focused on MHC haplotypes present in Holstein-Friesian cattle. This was primarily because these high-yielding dairy cattle and their crosses with indigenous cattle continue to increase in numbers in regions of Africa where ECF occurs. These high-value animals are considered critical to improving agricultural production and meeting the growing local demand for dairy products. As an ‘exotic’ breed that shows minimal tolerance to T. parva infections, these animals are particularly susceptible to ECF and are, therefore, the primary target for any vaccine against T. parva. Secondly, most previous antigen-identification work has primarily focused on animals expressing MHC haplotypes present in Holstein-Friesian animals. Thirdly, at the moment there is insufficient data on the diversity and frequency of MHC genotypes in cattle breeds indigenous to T. parva-endemic regions on which to base the selection of samples to subject to immunopeptidomic analysis in these populations. This fundamental gap in our knowledge is currently being addressed by several groups through the development and application of high-throughput sequencing approaches for exploring MHC diversity in a range of cattle populations [In this study, we exploited the features of  samples ,20,21. T samples ,38,47, wulations ,48,49.T. parva-derived peptide datasets obtained during this study was the predominance of peptides that were of noncanonical length and/or predicted to be non-MHC binders. Equivalent anomalous peptides were not a feature of the bovine peptidomes, suggesting that these anomalous peptides were specific to the pathogen rather than a technical fault in the protocol. Non-genuine MHC binders that co-precipitate with pMHC molecules in elution studies using equivalent protocols have been documented before . The second and third most common proteins acting as sources of co-precipitants in both datasets were histones , whilst the most commonly represented family of proteins were ribosomal proteins . Together, TpMuguga_02g00758, histones, and ribosomes constituted >80% of the co-precipitants identified in both datasets. A consistent feature of the BoLA-I and BoLA-DR in HIV-1 ). HoweveTheileria . Coprecipitants were not specifically examined in any of the other parasite immunopeptidomic studies. However, direct comparison between the studies would perhaps be confounded by technical differences; in the Toxoplasma gondii study [Plasmodium study [T. parva may reflect fundamental biological differences between it and the other parasites for which immunopeptidomic studies have been completed. Foremost of these is the location of T. parva schizonts free within the host cell cytoplasm . This feature, combined with the active secretion of Theileria proteins into the host cell cytoplasm [T. parva proteins to non-specifically bind to MHC molecules and/or integrate into elements of the host cell’s structures that are subsequently co-precipitated during the elution protocol. Notably, the TpMuguga_02g00758 gene product has a signal peptide, indicating that it is likely to be secreted into the host-cell cytoplasm. However, absence of any other annotated features of this protein precludes any inferences about its biological function and how this might relate to it being the major co-precipitant in both the BoLA-I and BoLA-DR datasets. A second pertinent feature of T. parva biology is the transformation of the host cell and rapid proliferation of both the host cell and parasite. To sustain the DNA and protein production that proliferation requires, histones and ribosomes are likely to be subject to high levels of expression and turn-over which may contribute to their overrepresentation as coprecipitants. At present there is no proteome of the schizont stage of T. parva, however transcriptomic analyses of T. parva schizonts show that several histone and ribosomal proteins are amongst the most abundantly expressed proteins [T. annulata has shown a high level of concordance—suggesting the transcriptome is quantitatively representative of the T. parva schizont proteome [Relatively few immunopeptidomic studies have been conducted on parasite-infected cells ,20,21, sytoplasm ,52,53 anytoplasm  and the proteins . Compariproteome .+ and CD8+ T-cell epitopes from T. parva proteins have predicted % rank binding scores within the respective 5% and 2% thresholds [After removal of co-precipitants, both the BoLA-I and BoLA-DR datasets showed bias for peptides that were predicted to be strong MHC-binders B, demonsresholds ,38, suppT. parva peptides identified from the first 3 BoLA-I samples only 1 out of 33 of the peptides (3.3%) was recognised. Prima facie, this lack of recognition was disappointing. However, it is similar to results from other immunopeptidomic studies . Thus, with regards to using immunopeptidomics to identify novel vaccine candidate antigens, the concept of using recognition of peptides by T-cells from immune animals to ‘validate’ peptides is conceptually flawed. To discount peptides not recognised by T-cells of immune cattle would mitigate against a major benefit of immunopeptidomics, the capacity to identify a large repertoire of MHC-presented peptides, amongst which may be potential epitopes that cannot be identified from T-cell screening but which potentially have the capacity to confer protection when utilised in vaccines. Data from recent studies of human melanoma [T. parva epitopes that are not recognised following natural infection. Following Adenovirus/MVA heterologous prime-boost immunisation of 4 calves with T. parva antigens  all 4 animals expressing either BoLA-DRB3*11:01 and/or 10:01 generated CD4+ T-cell responses specific for the Tp10 (TpMuguga_04g00772) antigen, which had not been previously observed as a CD4+ T-cell antigen in ITM-immunised animals bearing these or other BoLA-DR alleles [+ T-cells were capable of recognising T. parva-infected cells and, as far as could be determined, were functionally similar to CD4+ T-cells against immunodominant epitopes induced by ITM. Recognition by T-cells from immune animals has been used as a standard component of assays attempting to ‘validate’ peptides identified from immunopeptidomic studies. On assaying a subset of the s e.g., ,58) and  and T. ps  had already been defined. However, only the BoLA-A14-restricted TpMuguga_02g0089567–75 epitope was identified in the immunopeptidome (as a nested peptide within a 15-mer). This most likely was a reflection that the immunopeptidomes characterised in this study were only partial. In the final dataset the average number of T. parva unique peptides identified per BoLA-I and BoLA-DR sample was only ~6 and ~2.5 respectively. Examination of replicate samples demonstrated very limited overlap in the BoLA-I immunopeptidomes described  have not proved reliable for targeting antigen screening [T. parva [T. parva-endemic areas  preclude the direct application of immunopeptidomic studies to identify specific epitopes for all the potential permutations of MHC genotype and parasite strain. A more practicable approach would be to integrate this and future immunopeptidome data identifying proteins preferentially accessing the MHC processing pathways with data analysing the host immunogenetic and pathogen genetic diversity. The repertoire of bovine BoLA genotypes in cattle populations in T. parva-endemic areas is now being analysed using high-throughput sequencing approaches , and work has begun to develop improved immuno-informatics algorithms for cattle [T. parva strains [T. parva proteins to be assessed. Together, the outputs of these ‘omics’ technologies could be utilised to identify candidate T. parva antigens presented on BoLA-I/BoLA-II (immunopeptidomics) and evaluate them for their degree of conservation (genomics/transcriptomics of T. parva strains) and their content of peptides (immunoinformatics) that can be presented on multiple BoLA-I and BoLA-DR genotypes . The complexity of both the host MHC and pathogen strain diversity will undoubtedly make identification of a comprehensive set of candidate antigens immensely challenging, but this approach will make the best use of the data being generated from a suite of ‘omics technologies and provide a rational approach to identifying a ‘minimal’ set of antigens that would enable the induction of CD8+ and CD4+ T-cells from animals bearing a range of different MHC genotypes against diverse T. parva strains. The ultimate aim of the study was to assess the feasibility of immunopeptidomics to identify candidate antigens to include in novel subunit vaccines. At the end of the analysis, a total of 74 and 15 unique peptides with predicted high binding capacities for BoLA-I and BoLA-DR molecules were identified. This information can be used in multiple ways to inform candidate antigen selection. For example, as described in the final results section, immunopeptidomic data could increase the efficiency of conventional antigen screening by focusing on creening . The simT. parva ,9,10,11 r cattle ,38,47 en strains  has receT. parva-infected cells. The study encompasses data from multiple BoLA-I and BoLA-DR genotypes, identifying 74 and 15 unique BoLA-I and BoLA-DR binding peptides, and as such forms probably the most comprehensive immunopeptidomic analysis of any eukaryotic pathogen to date. However, there is clear evidence that the characterisation of the immunopeptidome completed herein is only partial and further studies are required to gain a more comprehensive understanding of the T. parva BoLA-I and BoLA-II immunopeptidomes. Based on the dataset obtained in this study, we have developed a simplified curation system that can be applied in future T. parva immunopeptidomic studies to effectively remove coprecipitating peptides that can dominate eluted peptide datasets and restrict the data to peptides that are predicted to be genuine MHC-binders. Due to the high levels of diversity in both T. parva and bovine MHC genotypes, we propose that the information derived from immunopeptidomics can most productively be used in the development of novel vaccines, not as a means to identify individual epitopes that are specific to a particular combination of T. parva strain and bovine MHC allele, but rather as part of an integrated strategy that aims to identify a repertoire of proteins that can potentially elicit CD4+ and CD8+ T-cell responses across a breadth of cattle MHC genotypes and that are able to provide protection against the spectrum of T. parva strains present in endemic areas. Such an approach will build on the synergy of the multiple new technologies that are being used to study T. parva and bovine immunogenetics and offer new opportunities to tackle the conundrum of T-cell antigen identification, which remains an obstacle to developing novel vaccines against this important disease.In this study, we have provided the first exploration of the immunopeptidome of


We aimed to evaluate the impact of applying dead-time correction on the reconstructed SPECT image versus on the acquisition projections before reconstruction.Dead-time correction is required for accurate quantitative SPECT-based dosimetry in the context of personalised 177Lu-filled phantom (up to 20.75 GBq) and dual-timepoint SPECT/CT in 14 patients treated with personalised 177Lu peptide receptor radionuclide therapy were analysed. Dead time was determined based on the acquisition wide-spectrum count rate for each projection and averaged for the entire acquisition. Three dead-time correction methods (DTCMs) were used: the per-projection correction, where each projection was individually corrected before reconstruction , and two per-volume methods using the average dead-time correction factor of the acquisition applied to all projections before reconstruction (DTCM2) or to the SPECT image after reconstruction (DTCM3). Relative differences in quantification were assessed for various volumes of interest (VOIs) on the phantom and patient SPECT images. In patients, the resulting dosimetry estimates for tissues of interest were also compared between DTCMs.Data from 16 SPECT/CT acquisitions of a decaying Both per-volume DTCMs (DTCM2 and DTCM3) were found to be equivalent, with VOI count differences not exceeding 0.8%. When comparing the per-volume post-reconstruction DTCM3 versus the per-projection pre-reconstruction DTCM1, differences in VOI counts and absorbed dose estimates did not exceed 2%, with very few exceptions. The largest absorbed dose deviation was observed for a kidney at 3.5%.While per-projection dead-time correction appears ideal for QSPECT, post-reconstruction correction is an acceptable alternative that is more practical to implement in the clinics, and that results in minimal deviations in quantitative accuracy and dosimetry estimates, as compared to the per-projection correction.The online version contains supplementary material available at 10.1186/s40658-022-00484-w. We p results .Our current dead-time correction method is based on the average observed wide-spectrum count rate during the SPECT acquisition, from which an average dead-time correction factor (DTCF) is deduced and applied to all voxels on the 3D reconstructed volume , 6. Howe177Lu-filled phantom and in patients undergoing personalised PRRT, as well as the impact of these methods on absorbed dose estimates for tissues of interest in patients.This study aimed to compare per-projection versus per-volume dead-time correction methods for QSPECT both in a Two dual-head SPECT/CT systems, namely a Symbia T6 (referred to as Symbia) and a Symbia Intevo 6 , both equipped with 9.5-mm-thick NaI(Tl) crystals and medium-energy low-penetration collimators were used.177Lu photopeak , lower and upper scatter windows (10% each), three additional energy windows were added to monitor the wide-spectrum count rate (18–680 keV), as previously described [In addition to the topeak 20 keV, 20%177Lu activity of 20.75 GBq on the Intevo system. Phantom acquisitions were performed consecutively with only detector 1 activated and with both detectors activated [A NEMA 2012/IEC 2008 phantom  was customised with a similar geometry as the one described in [177Lu-octreotate injection on the Symbia system. Acquisitions were performed with both detectors activated, as described above. The time per projection on Day 1 was either 15 or 20 s, while it was systematically 20 s per projection on Day 3.Data of 14 patients enrolled in our prospective clinical trial of personalised PRRT NCT02754297) were selected to gather a large range of average observed wide-spectrum count rate  phantom data from further studies. An acquisition was considered as saturated if some plateau or abrupt normal variations were observed on the count rate versus projection graph .Projections were reconstructed using SPECTRA Quant  with ordered subset expectation maximisation , CT-based attenuation correction, triple-energy window scatter correction and resolution recovery.For the phantom, CT-based volumes of interest (VOIs) were manually drawn around the saline bags and the external contour of the phantom. VOIs of the saline bags were automatically expanded by 0.5 cm in each direction to include spilled-out counts. The background activity within the extended VOI was removed as previously described to obtain the saline bags’ total recovered activity for subsequent analysis , 9. The L1–L4 vertebras were included.For the patients, as detailed in , 2-cm VOThe mean counts in these VOIs were converted to activity concentration using the calibration factor and the DTCF . For dosCF) and dead-time constant (τ) of the Symbia have previously been determined by Frezza et al. [RWo) is expressed in relationship with the activity (A) times RWo divided by the observed primary count rate (RPo) within the phantom VOI [The calibration factor  on the observed wide-spectrum count rate, using the original Sorenson’s equation:Once the dead-time constant is deduced, the DTCF can be determined as the ratio of the expected . Instead, we rounded each pixel counts to the upper integer with a probability equal to its four-digit decimal (using a Python 3.6 script). As this method is not completely deterministic, we performed this process in triplicate, followed by the reconstruction, to evaluate the effect of its randomness component. DTCM1 was considered the reference method, being in principle the most accurate.DTCM2—pre-reconstruction, per-volume correction Same method as DTCM1, but with all projections corrected using a single DTCF derived from the average observed wide-spectrum count rate of the entire acquisition. The purpose of DTCM2 was to rule out any bias introduced by performing the per-volume correction before versus after reconstruction, as in DTCM3.DTCM3—post-reconstruction, per-volume correction As detailed in [ailed in –6 and asBased on the dead-time constant, a look-up table returning a DTCF value for a given observed wide-spectrum count rate was created. Three dead-time correction methods (DTCM) were tested in the phantom and in patients:For each VOI on the phantom and patient images, the dead-time corrected counts per second according to each DTCM were quantified with the previously determined calibration factor. For the phantom, the estimated quantified activity concentrations for each VOI were also compared with the true activity concentrations. For DTCM1 and DTCM2, the coefficient of variation among the three repetitions was computed for each VOI. The relative differences in counts (and thus in activity concentration) and absorbed doses (in patients) were compared between the DTCMs. All statistics and graphs were generated on R 4.1 .n = 16).We first examined the observed wide-spectrum count rate per projection for the phantom  and previously described methods [The calibration factor and dead-time constant calculated using the valid reconstructed SPECT phantom data ( methods  were 10.n = 118), whereas those for all other VOIs did not exceed 0.81%. On this basis, we considered that the randomness with which fractional lost counts are distributed in projections before reconstruction has a negligible impact on quantification.To assess the impact of the random component involved in the methods by which the lost counts were injected into projections, DTCM1 and DTCM2 processing and subsequent reconstructions were done in triplicate for each dataset. For all VOIs under study, both in phantom and in patients, and for both DTCM1 and DTCM2, the median and the maximum coefficients of variation of the VOI counts were 0.27% and 1.50%, respectively. This maximum coefficient of variation was found for tumours in patients , adding lost counts before (i.e. DTCM2) or after (i.e. DTCM3) the reconstruction should be equivalent. For all VOIs under study, both in the phantom and in patients, the median and maximum relative differences in VOI counts between DTCM2 and DTCM3 were 0.06% and − 0.84%, respectively. We thus concluded that DTCM2 is indeed equivalent to DTCM3, and since it offers no advantage over the latter, it was not studied further.For the phantom, on a per-VOI basis, the largest median and maximum relative count differences between DTCM3 and DTCM1 were found for the 200-mL background VOI, at 1.08% and 2.38%, respectively. These values were smaller for the other VOIs: − 0.28% and − 1.49% for the large saline bag, 0.16% and 0.40% for the small saline bag, and − 0.18% and − 1.24% for the whole phantom  for tumours. The largest median and maximum relative dose differences between DTCM1 versus DTCM3 were 0.31% and 3.24%, respectively  self-absorbed doses were 1.29 (0.23–3.62) Gy for the bone marrow, 6.11 (4.01–9.79) Gy for the left kidney, 5.18 Gy (2.36–7.04) for the right kidney, 5.98 Gy (3.19–7.80) for both kidneys averaged, and 39.76 Gy  [177Lu instead of 99mTc and that we also analysed the impact of dead-time correction methods on patient QSPECT data and dosimetry estimates.To our knowledge, only Cohalan et alCF≈1.25) . They reCF≈1.25) . Such a . to correct projections for dead time. To achieve this, they inserted a script into the reconstruction loop of commercial software , while we injected lost counts into the acquisition file prior to reconstruction. However, this required in-house scripting in both cases and implied having access to editable reconstruction software or adding an image manipulation step, respectively. Conversely, post-reconstruction dead-time correction is more convenient to perform. While a script or batch file may ease computation of the average wide-spectrum count rate and the automatic look-up of the DTCF, these tasks can also easily be accomplished using common nuclear medicine and spreadsheet software.We used a different approach than that of Cohalan et alWe concur that, ideally, the most physically accurate dead-time correction method should be used, i.e. per-projection. Eventually, the latter may become standard in commercial QSPECT/CT systems that are being developed. Until then, post-reconstruction per-volume correction appears an appropriate and practical alternative that may be more easily amenable to wider implementation in the clinics. This is applicable to NaI crystal-based SPECT/CT systems. Of note, as newly introduced CZT-based SPECT/CT systems may exhibit less dead time, they should be even less influenced by the choice of the DTCM .As compared to per-projection dead-time correction, per-volume dead-time correction using a DTCF based on the average acquisition wide-spectrum count rate results in small differences in SPECT quantification and dosimetry estimates that rarely exceed 2%. Post-reconstruction dead-time correction, which is easier to implement, thus appears adequate for use in personalised radiopharmaceutical therapy.Additional file 1. NEMA phantom acquisitions total activity and averaged DTCF.


Introduction: The uplift of SARS-CoV-2 infection has necessitated the understanding of long-term consequences in the affected population. This study was driven by a lack of Indian studies to estimate the torment of post-coronavirus disease 2019 (COVID-19) symptoms and the quality of life.Methods: This prospective observational study was conducted in a tertiary care centre in South India, between August and November 2020. SARS-CoV-2 hospitalised patients were telephonically questioned regarding the persistence of symptoms along with an assessment of the quality of life using the 15D questionnaire in the first, second, and fourth weeks. Since a majority of the patients had resolution of symptoms by four weeks, the study was not extended for a longer period.Results: The study included 476 patients whose mean length of hospital stay was 7.67 days. Of the patients, 68.7% had mild severity, 24.8% had moderate disease, and 6.5% had severe disease. About 28.4% required oxygen, 8.2% required ICU care, and 1.3% required mechanical ventilation. Myalgia (13.9%), cough (1.3%), and dyspnoea (6.1%) were the predominant persistent symptoms in the fourth-week post-discharge. All the symptoms of health-related quality of life and physical performance improved by the fourth week, which was statistically significant.Conclusion: Our study findings are in stark contrast to the studies published from other regions of the world, which show a significant worsening of quality of life even among those with mild illness. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic was initially identified as a cluster of pneumonia cases in Wuhan, China . The mosThe local healthcare facilities in almost all countries have faced problems in terms of the need for large-scale testing of contacts and high-risk groups, a sudden increase in hospitalisations, the need for trained medical professionals, demand for large quantities of oxygen supply, increased demand for intensive care unit beds and ventilators, etc. Thus there was a visible strain on the finances of many countries as they had to allocate funds to create isolation and testing centres and fully equipped treatment centres even though the pandemic had a crippling effect on income generation as international trade took a hit with many countries closing their borders. From the patient’s perspective, many reported a range of problems post-illness, from minor complaints like fatigue to long-term exertional breathlessness, prolonged need for oxygen support, acute psychological stress, and acute kidney injury.In addition to the high mortality, the illness takes a protracted course manifesting a spectrum of symptoms, which include persistent fatigue, myalgia, breathlessness, alternation in cognitive function, and sleep disturbance, resulting in a poor quality of life even when the illness is mild ,4. In a We prospectively studied a cohort of patients who were admitted to the COVID-19 ward, high dependency unit, or critical care unit between August and November 2020 at our centre, a tertiary care teaching hospital located in Chennai, India. All participants who were more than 18 years of age with reverse transcription-polymerase chain reaction (RT-PCR)-confirmed SARS-CoV-2 infection were included in this study. Participants in the age group of less than 18 years and patients who had negative RT-PCR for SARS-CoV-2 infection but positive serology tests were excluded. Patients discharged without fulfilling the discharge criteria, including against medical advice, were also excluded from this study. The sampling method used was consecutive sampling. The sample size of the study was not predetermined since it was a period-dependent cohort study over four months. About 492 patients who consented to a follow-up of their symptoms were enrolled in this study. Around 16 participants were later excluded due to withdrawal of consent or loss of follow-up. The study was approved by the Institutional Ethics Committee of Sri Ramachandra University (IEC-NI/20/SEP/75/79) and oral telephonic consent was obtained from the study participants. Each study participant was assessed by one of the study investigators using a questionnaire, which sought information on age, gender, prior medical illness like underlying bronchial asthma and chronic obstructive pulmonary disease, and presenting symptoms. The patients' comorbid conditions like diabetes mellitus, systemic hypertension, coronary heart disease, chronic kidney diseases, and cerebrovascular accidents were noted. We then prospectively obtained information on laboratory parameters, medications received, and complications during the stay from in-patient medical records. The severity of illness was classified as mild , moderate (saturation: 94-90%), and severe (saturation < 90%), as defined by the Ministry of Health and Family Welfare (MOHWF)/Indian Council of Medical Research (ICMR) [Chronic symptoms present prior to SARS-COV-2 infection were excluded from the follow-up assessment. Hence, only the new onset symptoms, which were likely due to COVID-19 infection, were followed up. Enrolled participants were followed up for four weeks post-discharge, and were telephonically questioned regarding the persistence or development of symptoms in the first, second, and fourth weeks. In addition to follow-up of symptoms present during hospitalisation , symptoms which could indicate long COVID-19 were assessed. We used seven of the 15 components of the "15D quality of life questionnaire" relevant to post-acute SARS-CoV-2 infection . Each ofCategorical variables were expressed as numbers (percentage) and continuous variables were expressed as mean (standard deviation). Categorical variables were compared using the chi-square test and continuous variables with a one-way analysis of variance (ANOVA). Analysis was done using SPSS  version 23.0 . A p-value < 0.05 was considered significant.Around 492 patients who consented to follow-up of their symptoms were enrolled from August 2020 to November 2020. About 16 patients were later excluded due to withdrawal of consent or loss of follow-up. The response rate of the study was 96.3%. The total number of patients who were included in this study was 476. The mean age of the studied patients was 48.84 years (range: 19-85 years), and most were males (63.4%). The mean length of hospital stay was 7.67 days. Most participants had mild severity , followed by moderate  and severe disease . About 28.4% (n = 135) required oxygen, 8.2% (n = 39) required ICU care, and 1.3% (n = 6) required mechanical ventilation. Table The follow-up of symptoms in the study cohort during the second and fourth weeks is shown in Table In the next part of our study, we also observed the health-related quality of life and physical performance in our study patients post-discharge at the end of the first, second, and fourth week based on the 15D instrument of health-related quality of life (recommended by the Washington Panel) . We consHealth-related assessment of participants showed that half of them (51%) had some form of walking difficulty requiring support. However, we excluded confounding variables like osteoarthritis and old age while assessing for difficulty in walking. None of the patients was bedridden or unable to move. At the end of the fourth week, 84% of the patients were able to walk without any difficulty. The cohort showed good recovery of breathlessness with only 1.1% having dyspnoea on walking on level ground. About one-fifth (21.9%) had sleep disturbance at week one, which decreased to 13% at week four. Close to half of the cohort (48.9%) required support for daily activity in the first week, which improved to 9.9% at four weeks. At the end of the first week, 13% were affected with mild to severe depression, which decreased to 4.2% at week four. The majority of patients were able to eat without any assistance.Though most patients recover completely from the acute SARS-CoV-2 infection, a few people experience persistent symptoms, including breathlessness, cough, fatigue, sleep disturbances, psychological problems like depression or anxiety, and a poor quality of life. The "Post-COVID Syndrome" includes persistent symptoms, which could be due to residual inflammation, organ damage, prolonged hospitalisation, prolonged ventilation, social isolation, or impact on pre-existing health conditions . Our stuAt the end of four weeks, the symptoms which persisted were myalgia 13.9%), cough (1.3%), dyspnoea (6.1%), altered smell (0.6%), and loss of taste (1.1%). In comparison, observations from Spain and Italy showed persisting fatigue (30.5-53.1%) and dyspnoea (31.4-43.4%) ,10,13. A.9%, cougAt the end of the first-week post-discharge, the dimension of life that was most affected was mobility and activities of daily life (51.1%), followed by breathing (39.1%) and sleep (21.8%). At the end of the fourth-week post-discharge, the percentage affected decreased to 16% for mobility, 13% for sleep, 12.6% for breathlessness, and 9.9% for activities of daily life. Concerning the health-related quality of life and physical performance in our study patients post-discharge at the end of the first, second, and fourth week, all the symptoms improved at the end of the fourth-week post-discharge, which was statistically significant. Studies have shown a similar prevalence of dimensions of quality of life  between ICU and non-ICU patients, but for a higher prevalence of pain in the ICU care group . SimilarSeveral studies with genome-wide association analysis (GWAS) reveal that several genetic factors play a vital role in the host susceptibility to SARS-CoV-2 infection influencing the disease severity and progression . A genomLimitations of this study include response bias or misreporting of data. Since this was a preliminary study on the Wuhan variant and lack of available studies assessing the immunological and genetic aspects in our population, we are unable to give the reasoning for the low prevalence of long COVID-19 symptoms. This study is a single-centre study and cannot be generalised to all populations. Several such studies are required in larger cohorts in more moderate to severe COVID-19 patients to analyse the post-COVID-19 sequelae and physical and mental quality of life in patients affected by SARS-CoV-2 infection.It is very well evident that SARS-CoV-2 infection is dynamic, and further studies and global data are required to understand the impact of the disease on the physical and mental well-being of the affected population. The persistent symptoms and quality of life post-COVID-19 need a long-term follow-up for a better understanding of the nature of the disease and to give better guidance for patient care.